ferrotorch

Top-level re-export crate for the ferrotorch deep learning framework.

What it provides

This is the umbrella crate that re-exports all ferrotorch sub-crates through a single dependency. Add ferrotorch to your Cargo.toml and access everything via submodules:

Always included

• ferrotorch-core — Tensor, autograd, differentiable ops (40+), quantization, einops
• ferrotorch-nn — Module trait, 26+ layers (Linear, Conv1d/2d, LSTM, GRU, Attention, norms, activations), losses, LoRA
• ferrotorch-optim — 8 optimizers (SGD, Adam, AdamW, RMSprop, Adagrad, L-BFGS, Muon, K-FAC), gradient clipping, schedulers
• ferrotorch-data — Dataset, DataLoader (parallel via rayon), DistributedSampler, collation, transforms
• ferrotorch-vision — ResNet, VGG, ViT, Swin, ConvNeXt, EfficientNet, YOLO; MNIST/CIFAR datasets; image I/O

Default features (opt-out with default-features = false)

• ferrotorch-train — Learner training loop, callbacks, metrics, checkpointing
• ferrotorch-serialize — ONNX export, PyTorch .pt import, safetensors, GGUF
• ferrotorch-jit — Tracing JIT, IR graph, optimization passes, code generation
• ferrotorch-distributions — Probability distributions for sampling and VI
• ferrotorch-profiler — Performance profiling with Chrome trace export
• ferrotorch-hub — Model hub for downloading and caching pretrained weights

Optional features (opt-in)

• gpu — CUDA backend with hand-written PTX kernels and cuBLAS (cargo add ferrotorch --features gpu)
• cubecl — Portable GPU via CubeCL: CUDA + WGPU/AMD + ROCm (--features cubecl)
• distributed — DDP, collective ops, TCP backend (--features distributed)

Quick start

use ferrotorch::prelude::*;

fn main() -> FerrotorchResult<()> {
    // Autograd
    let a = scalar(2.0f32)?.requires_grad_(true);
    let b = scalar(3.0f32)?.requires_grad_(true);
    let c = (&a * &b)?;
    c.backward()?;
    println!("{}", a.grad()?.unwrap()); // tensor(3.)

    // Neural network
    let model = Sequential::new(vec![
        Box::new(Linear::new(784, 256, true)?),
        Box::new(ReLU::default()),
        Box::new(Linear::new(256, 10, true)?),
    ]);
    let x = rand::<f32>(&[32, 784])?;
    let out = model.forward(&x)?;
    Ok(())
}

Submodules

use ferrotorch::nn::*;           // layers, losses, activations
use ferrotorch::optim::*;        // optimizers, schedulers
use ferrotorch::data::*;         // datasets, dataloaders
use ferrotorch::vision::*;       // models, transforms
use ferrotorch::train::*;        // Learner, callbacks
use ferrotorch::serialize::*;    // ONNX, safetensors
use ferrotorch::jit::*;          // tracing, IR
use ferrotorch::distributions::*; // probability distributions

Part of ferrotorch

This is the top-level crate of the ferrotorch workspace. See the workspace README for full documentation.

License

MIT OR Apache-2.0